Triple
T914033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actress |
E19726
|
entity |
| Predicate | hasStatueMaterial |
P1272
|
FINISHED |
| Object | gold-plated bronze |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: gold-plated bronze | Statement: [Academy Award for Best Actress, hasStatueMaterial, gold-plated bronze]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStatueMaterial Context triple: [Academy Award for Best Actress, hasStatueMaterial, gold-plated bronze]
-
A.
hasStatue
Indicates that one entity possesses, contains, or is associated with a statue representing or located within it.
-
B.
statuetteShape
Indicates that one entity has the physical form or outline of a statuette.
-
C.
materialUsed
chosen
Indicates that one entity is made from, incorporates, or utilizes the other entity as its material or substance.
-
D.
hasMaterialType
Indicates that something is composed of, made from, or characterized by a specific type of material.
-
E.
statueHeight
Indicates the height measurement of a statue in some specified unit.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b6755c488190b7f7848110e3ea2c |
completed | March 1, 2026, 9:58 p.m. |
| PD | Predicate disambiguation | batch_69a4b292d3408190947cbc2f794cf8c5 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:39 p.m.